Robust Symbolic Regression for Network Trajectory Inference

Abstract

Real-world complex systems often miss high-fidelity physical descriptions and are typically subject to partial observability. Learning dynamics of such systems is a challenging and ubiquitous problem, encountered in diverse critical applications which require interpretability and qualitative guarantees. Our paper addresses this problem in the case of probability distribution flows governed by ODEs. Specifically, we devise a *white box* approach -dubbed Symbolic Distribution Flow Learner ($\texttt{SDFL}$)- combining symbolic search with a Wasserstein-based loss function, resulting in a robust model-recovery scheme which naturally lends itself to cope with partial observability. Additionally, we furnish the proposed framework with theoretical guarantees on the number of required *snapshots* to achieve a certain level of fidelity in the model-discovery. We illustrate the performance of the proposed scheme on the prototypical problem of Kuramoto networks and a standard benchmark of single-cell RNA sequence trajectory data. The numerical experiments demonstrate the competitive performance of $\texttt{SDFL}$ in comparison to the state-of-the-art.